Open AccessA new localization method based on improved particle swarm optimization for wireless sensor networks
Author(s) -
Yang Qiaohe
Publication year - 2022
Publication title -
iet software
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.305
H-Index - 43
eISSN - 1751-8814
pISSN - 1751-8806
DOI - 10.1049/sfw2.12027
Subject(s) - particle swarm optimization , wireless sensor network , convergence (economics) , node (physics) , ranging , computer science , swarm behaviour , inertia , real time computing , process (computing) , algorithm , wireless , mathematical optimization , engineering , artificial intelligence , mathematics , computer network , telecommunications , physics , structural engineering , classical mechanics , economics , economic growth , operating system
Wireless sensor network (WSN) node localisation technology based on received signal strength indication (RSSI) is widely used as it does not need additional hardware devices. The ranging accuracy of RSSI is poor, and the particle swarm optimisation (PSO) algorithm can effectively improve the positioning accuracy of RSSI. However, the particle swarm diversity of the PSO algorithm is easy to lose quickly and fall into local optimal solution in the iterative process. Based on the convergence conditions and initial search space characteristics of the PSO algorithm in WSN localisation, an improved PSO algorithm (improved self‐adaptive inertia weight particle swarm optimisation [ISAPSO]) is proposed. Compared with the other two PSO location estimation algorithms, the ISAPSO location estimation algorithm has good performance in positioning accuracy, power consumption and real‐time performance under different beacon node proportions, node densities and ranging errors.